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Individual sequences in large sets
of gene sequences may be
distinguished efficiently by
combinations of shared subsequences
Mark J Gibbs, John S Armstrong and Adrian J Gibbs
BMC Bioinformatics 2005, 6:90
Presented by Miguel Gonzalez
Outline

Background

Results

Discussion
Background

Organism identification

Comparative Gene Sequencing

DNA probes
The Problem!

Using contemporary biological research is too
time consuming and expensive.

Usually complex techniques are involved.
The Solution


Develop a method for identifying sequences that
is not extremely specific.
Probes can be found that bind to more than
target sequence to produce unique binding
patterns or fingerprints.
Hypothesis

To develop a method for identifying sequences
efficiently using distinguishing sub-sequences
(DSSs).
Strategy


The study uses the methods of taxonomy where
combinations of characters shared by different
members of a target organisms.
The advantage is that identification requires
fewer characters and questions to identify an
individual target.
Strategy


The minimum number of characters for this
method is defined by the binary logarithm
X = log2Y,
X = # of characters;
Y = # of targets
Ex. 10 characters could identify a set of 1024
targets.
Testing Hypothesis




Three sets of cytochrome oxidase c 1 (CO1)
sequences were used: animal, insect, and moth
CO1-animal had 96 species
CO1-insect had 92 species
CO1-moth had 201 species
Target Sequence



ClustalX was performed on the 3 sets of
sequences to find a target region within
sequences.
Pools of sub-sequences were created ranging
from lengths of 6-31 nucleotides
From the sub-sequences, distinguishing subsequences were identified
Results
Results
Results
Discussion


A method was produced where sub-sequences
are found which, distinguish the gene sequences
or groups of gene sequences from which they
came from.
Sequence diversity and sub-sequence length
were found to be major factors influencing the
number of subsequences available as probe
targets.
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